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Random Trees
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Download or read book Random Trees written by Michael Drmota and published by Springer Science & Business Media. This book was released on 2009-04-16 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide a thorough introduction to various aspects of trees in random settings and a systematic treatment of the mathematical analysis techniques involved. It should serve as a reference book as well as a basis for future research.
Download or read book Random Trees written by Michael Drmota and published by Springer. This book was released on 2009-08-29 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide a thorough introduction to various aspects of trees in random settings and a systematic treatment of the mathematical analysis techniques involved. It should serve as a reference book as well as a basis for future research.
Book Synopsis Random Generation of Trees by : Laurent Alonso
Download or read book Random Generation of Trees written by Laurent Alonso and published by Springer Science & Business Media. This book was released on 1994-12-31 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random Generation of Trees is about a field on the crossroads between computer science, combinatorics and probability theory. Computer scientists need random generators for performance analysis, simulation, image synthesis, etc. In this context random generation of trees is of particular interest. The algorithms presented here are efficient and easy to code. Some aspects of Horton--Strahler numbers, programs written in C and pictures are presented in the appendices. The complexity analysis is done rigorously both in the worst and average cases. Random Generation of Trees is intended for students in computer science and applied mathematics as well as researchers interested in random generation.
Book Synopsis Probability on Trees and Networks by : Russell Lyons
Download or read book Probability on Trees and Networks written by Russell Lyons and published by Cambridge University Press. This book was released on 2017-01-20 with total page 1023 pages. Available in PDF, EPUB and Kindle. Book excerpt: Starting around the late 1950s, several research communities began relating the geometry of graphs to stochastic processes on these graphs. This book, twenty years in the making, ties together research in the field, encompassing work on percolation, isoperimetric inequalities, eigenvalues, transition probabilities, and random walks. Written by two leading researchers, the text emphasizes intuition, while giving complete proofs and more than 850 exercises. Many recent developments, in which the authors have played a leading role, are discussed, including percolation on trees and Cayley graphs, uniform spanning forests, the mass-transport technique, and connections on random walks on graphs to embedding in Hilbert space. This state-of-the-art account of probability on networks will be indispensable for graduate students and researchers alike.
Book Synopsis Trees of North America and Europe by : Roger Phillips
Download or read book Trees of North America and Europe written by Roger Phillips and published by New York : Random House. This book was released on 1978 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This splendid guide to tree identification contains more than 1,000 full-color photographs. Each tree is illustrated in full detail -- by leaf, flower, fruit, bark, and mature tree shape -- and is fully described in the text. A unique leaf index makes the identification of trees simple and accurate. The trees are arranged alphabetically by Latin name and an index of common names concludes the book. An indispensable companion for both the enthusiast and the botanist.
Book Synopsis Computational Genomics with R by : Altuna Akalin
Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Book Synopsis Random Forests with R by : Robin Genuer
Download or read book Random Forests with R written by Robin Genuer and published by Springer Nature. This book was released on 2020-09-10 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.
Book Synopsis Decision Trees and Random Forests by : Mark Koning
Download or read book Decision Trees and Random Forests written by Mark Koning and published by Independently Published. This book was released on 2017-10-04 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services. They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you.
Book Synopsis Evolution of Random Search Trees by : Hosam M. Mahmoud
Download or read book Evolution of Random Search Trees written by Hosam M. Mahmoud and published by Wiley-Interscience. This book was released on 1992 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: While several excellent books have been written on algorithms and their analysis, remarkably few have been dedicated to the probabilistic analysis of algorithms. This graduate text/professional reference fills that gap and brings together material that is scattered over tens of publications. Its unifying theme is the study of some classes of random search trees suitable for use as data structures with a behavior of random growth that is almost as good as balanced trees.
Book Synopsis Tree-based Machine Learning Algorithms by : Clinton Sheppard
Download or read book Tree-based Machine Learning Algorithms written by Clinton Sheppard and published by Createspace Independent Publishing Platform. This book was released on 2017-09-09 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learn how to use decision trees and random forests for classification and regression, their respective limitations, and how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you."--Back cover.
Book Synopsis Probability and Real Trees by : Steven N. Evans
Download or read book Probability and Real Trees written by Steven N. Evans and published by Springer. This book was released on 2007-09-26 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random trees and tree-valued stochastic processes are of particular importance in many fields. Using the framework of abstract "tree-like" metric spaces and ideas from metric geometry, Evans and his collaborators have recently pioneered an approach to studying the asymptotic behavior of such objects when the number of vertices goes to infinity. This publication surveys the relevant mathematical background and present some selected applications of the theory.
Book Synopsis Machine Learning by : Steven W. Knox
Download or read book Machine Learning written by Steven W. Knox and published by John Wiley & Sons. This book was released on 2018-03-08 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.
Book Synopsis Tangled Trees by : Roderic D. M. Page
Download or read book Tangled Trees written by Roderic D. M. Page and published by University of Chicago Press. This book was released on 2003 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, the use of molecular data to build phylogenetic trees and sophisticated computer-aided techniques to analyze them have led to a revolution in the study of cospeciation. Tangled Trees provides an up-to-date review and synthesis of current knowledge about phylogeny, cospeciation, and coevolution. The opening chapters present various methodological and theoretical approaches, ranging from the well-known parsimony approach to "jungles" and Bayesian statistical models. Then a series of empirical chapters discusses detailed studies of cospeciation involving vertebrate hosts and their parasites, including nematodes, viruses, and lice. Tangled Trees will be welcomed by researchers in a wide variety of fields, from parasitology and ecology to systematics and evolutionary biology. Contributors: Sarah Al-Tamimi, Michael A. Charleston, Dale H. Clayton, James W. Demastes, Russell D. Gray, Mark S. Hafner, John P. Huelsenbeck, J.-P. Hugot, Kevin P. Johnson, Peter Kabat, Bret Larget, Joanne Martin, Yannis Michalakis, Roderic D. M. Page, Ricardo L. Palma, Adrian M. Paterson, Susan L. Perkins, Andy Purvis, Bruce Rannala, David L. Reed, Fredrik Ronquist, Theresa A. Spradling, Jason Taylor, Michael Tristem
Book Synopsis Clusters, Orders, and Trees: Methods and Applications by : Fuad Aleskerov
Download or read book Clusters, Orders, and Trees: Methods and Applications written by Fuad Aleskerov and published by Springer. This book was released on 2014-06-11 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data. This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions.
Book Synopsis Machine Learning and Data Mining in Pattern Recognition by : Petra Perner
Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer. This book was released on 2012-07-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
Book Synopsis Hands-On Machine Learning with R by : Brad Boehmke
Download or read book Hands-On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Download or read book AI Meets BI written by Lakshman Bulusu and published by CRC Press. This book was released on 2020-11-03 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.
